Phi-4-mini-reasoning vs. DeepCoder

Phi-4-mini-reasoning is an advanced transformer-based language model that boasts 3.8 billion parameters, tailored specifically for superior performance in mathematical reasoning and systematic problem-solving, especially in scenarios with limited computational resources and low latency. The model's optimization is achieved through fine-tuning with synthetic data generated by the DeepSeek-R1 model, which effectively balances performance and intricate reasoning skills. Having been trained on a diverse set of over one million math problems that vary from middle school level to Ph.D. complexity, Phi-4-mini-reasoning outperforms its foundational model by generating extensive sentences across numerous evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1 in various tasks. Additionally, it features a 128K-token context window and supports function calling, which ensures smooth integration with different external tools and APIs. This model can also be quantized using the Microsoft Olive or Apple MLX Framework, making it deployable on a wide range of edge devices such as IoT devices, laptops, and smartphones. Furthermore, its design not only enhances accessibility for users but also opens up new avenues for innovative applications in the realm of mathematics, potentially revolutionizing how such problems are approached and solved.

DeepCoder, a fully open-source initiative for code reasoning and generation, has been created through a collaboration between the Agentica Project and Together AI. Built on the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has been fine-tuned using distributed reinforcement learning techniques, achieving an impressive accuracy of 60.6% on LiveCodeBench, which represents an 8% improvement compared to its predecessor. This remarkable performance positions it competitively alongside proprietary models such as o3-mini (2025-01-031 Low) and o1, all while operating with a streamlined 14 billion parameters. The training process was intensive, lasting 2.5 weeks on a fleet of 32 H100 GPUs and utilizing a meticulously curated dataset comprising around 24,000 coding challenges obtained from reliable sources such as TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each coding challenge was required to include a valid solution paired with at least five unit tests to ensure robustness during the reinforcement learning phase. Additionally, DeepCoder employs innovative methods like iterative context lengthening and overlong filtering to effectively handle long-range contextual dependencies, allowing it to tackle complex coding tasks with proficiency. This distinctive approach not only enhances DeepCoder's accuracy and reliability in code generation but also positions it as a significant player in the landscape of code generation models. As a result, developers can rely on its capabilities for diverse programming challenges.